matplotlib-devel Mailing List for matplotlib

Hi all, here's my first patch for matplotlib. Someone noticed at Stack
Overflow that the plot_surface function in mplot3d wasn't especially fast
for a lot of points (and small rstrides/cstrides) and using shading and a
single color. I found some parts of the code that weren't vectorized. These
are my changes so far.

Summary of changes:
1. Changed from double looping over aranges to using xrange
2. Made the normalization of the normals and their dot product with the
vector [-1,-1,0.5] to find the shading a vectorized operation.
3. Changed a list comprehension which calculated the colors using an
iterative approach to using the already built-in vectorization of the
Normalization class and using the np.outer function. The result is a numpy
array rather than a list which actually speeds up things down the line.
4. removed the corners array from plot_surface which wasn't ever used or
returned. It didn't really slow things down, but I'm thinking that it is
cruft.

For change number two, I made a separate function that generates the shades,
but feel free to move that around if you prefer.. or maybe it should be a
function that begins with a _ because it shouldn't be used externally. These
changes give varying levels of speed improvement depending on the number of
points and the rstrides/cstrides arguments. With larger numbers of points
and small rstrides/cstrides, these changes can more than halve the running
time. I have found no difference in output after my changes.

I know there is more work to be done within the plot_surface function and
I'll submit more changes soon.

Justin

On Sun, Nov 14, 2010 at 7:25 PM, Jae-Joon Lee <lee...@gm...> wrote:

> Hi,
>
> I just committed a simple change that disables pstoeps in ps backend
> when "xpdf" distiller is used. Without this, output bbox was incorrect
> with "xpdf" distiller and usetex=False.
>
> Getting the correct bbox with the ps backend is very tricky, and it'll
> be appreciated if others help me test this.
>
> Here is a simple test script (this needs to be run with most recent
> svn version of matplotlib, and you need to able to use ustex=True!).
> It will produce 6 eps files. Please check if any of the eps files have
> incorrect bbox. And report which file has wrong bbox and your setup :
> your OS, gs version number (output from "gs -v"), pdftops version
> number (output from "pdftops -v"). It will be also good if you report
> your setup even when everything is okay.
>
> Regards,
>
> -JJ
>
> import matplotlib.pyplot as plt
>
> plt.plot([1,2])
>
> for distiller in ["False", "xpdf", "ghostscript"]:
>    for usetex in ["True", "False"]:
>        plt.rcParams["ps.usedistiller"]=distiller
>        plt.rcParams["text.usetex"]=usetex
>
>        plt.savefig("test_bbox_%s_%s.eps" % (distiller, usetex))
>
>
Evince shows a few of the eps files weirdly:

test_bbox_ghostscript_False.eps comes out as 8.5x11 inch while the other
come out as 8x6 inches.
test_bbox_xpdf_False.eps is the correct size, but the graph was placed as if
it was centered on a 8.5x11 page, thereby cutting off the bottom half of the
graph.

The system:

Fedora 13
pdftops 0.12.4
ghostscript 8.71
evince 2.30.3 (using libspectre 0.2.4)

I will try out my Ubuntu system tonight.

Ben Root